Road deaths by country

Scatter plot

Photo by Yannis H on Unsplash

Some car accidents are caused by the ignorance or disbelief of the fact that a driver’s eyes and mind can be thousands of kilometres apart…
— Mokokoma Mokhonoana

Ingest the data

gdp and road traffic accidents

# Load csv data file
df_gdp <- read.csv("archetypes/road-deaths-by-country/gdp.csv", header = TRUE, stringsAsFactors = FALSE, encoding = "UTF-8")
df_gdp

df_rd <- read.csv("archetypes/road-deaths-by-country/road-deaths.csv", header = TRUE, stringsAsFactors = FALSE, encoding = "UTF-8")
df_rd

Wrangle the data

bracket removal and more

df_rd_wrangle <- df_rd %>% mutate(road_deaths = as.integer(str_replace(est_road_traffic_death_rate_per_100k_population_both_sexes, "\\[.+?\\]", "")))
df_rd_wrangle <- df_rd_wrangle %>% filter(year == 2017)
df_rd_wrangle <- df_rd_wrangle %>% select(c(country, road_deaths))
df_rd_wrangle$iso3 <- countrycode(df_rd_wrangle$country, origin='country.name', destination='iso3c')
# df_rd_wrangle

df_combined <- merge(df_rd_wrangle, df_gdp, by.x='iso3', by.y='Country_Code')
df_combined$region <- countrycode(df_combined$iso3, origin='iso3c', destination='un.region.name')
df_combined <- df_combined %>% select(c(iso3, country, road_deaths, X2017, region))
df_combined <- df_combined[complete.cases(df_combined), ]
# df_combined

df_final <- df_combined %>% rename(gdp = X2017)
df_final <- df_final %>% rename_with(toupper)
df_final <- df_final %>% filter(ROAD_DEATHS > 0)
df_final

Plot

data points with smoothing

theme_opts <- theme(
    text = element_text(family = "inconsolata", size = 16), 
    plot.title = element_text(color = "black", size = 16, face = "bold"),
    plot.subtitle = element_text(color = "black", size = 12),
    plot.caption = element_text(color = "#555555", size = 10),
    # axis.title.x = element_blank(),
    # axis.title.y = element_blank(),
    # axis.text.x = element_text(vjust = 12),
    panel.border = element_blank(),
    panel.background = element_blank(),
    # panel.grid.minor = element_blank(),
    # panel.grid.major = element_blank(),
    legend.position='top'
  )

# Plot
v1 <- ggplot(df_final, aes(x = GDP, y = ROAD_DEATHS)) + 
  geom_point(aes( fill = REGION, color = REGION), size = 4, shape = 21) +
  geom_smooth(method="loess", se = FALSE, linetype = 'solid', size = 2, color = '#82A0C2') + # "lm", "glm", "gam", "loess" 
  geom_text_repel(aes(label = COUNTRY)) + 
  scale_x_log10() +
  scale_y_continuous() +
  labs( title = "Driving lessons",
        subtitle = NULL,
        caption = "Source: World Bank, IHME",
        x = "GDP per capita, PPP (constant 2017 international $)",
        y = "Est road traffic death rate per 100k population both sexes, 2017") +
  theme_bw() +
  theme_opts

girafe(ggobj = v1, width_svg = 1280/72, height_svg = 720/72,
       options = list(opts_sizing(rescale = TRUE, width = 0.75))
)

Plot with annotations

some narrative to help tell the story

Save plot

as vector or raster image

References

citations for narrative and data sources

Narrative Inspiration: The Economist, Driving lession
Data Source: World Health Organization, https://apps.who.int/gho/data/node.main.A997